Congestion under Investigation

Transcription

1 Congestion under Investigation Dr. Sven Maerivoet 15/02/2016 Seminar Mathematical Engineering Techniques 1

2 Overview Introducing Transport & Mobility Leuven (TML) S. Maerivoet Congestion under Investigation 2

3 Background of the company Founded in 2002 as a spin-off company (NV): Catholic University of Leuven TNO research institute (The Netherlands) S. Maerivoet Congestion under Investigation 3

4 The people at TML Multidisciplinary team (18 people): Transport, bio-, chemical, and environmental engineering Economy Computer science Psychology Areas of expertise: Transport Environment Admin Managing director Economics Transport economics (incl. impact assessments, SCBAs, ) Traffic flow theory (incl. ITS measures, congestion estimates). Transport analyses (private road, rail, public transport, IWW, air, ) Environment, public health, Traffic safety (incl. legislation, infrastructure, veh. technology, ) Spatial economics (incl. regional development) S. Maerivoet Congestion under Investigation 4

5 Overview of our activities TML conducts research and studies. Strongly quantitative (prediction models, simulation techniques,...) Policy support (we can influence policy) Integration of mobility, environment, and economics Combining both fundamental and applied research by linking theoretical findings with practical knowledge Bridge between university and society Independent and open policy City and regional policies, Belgium (federal, Flanders, and Brussels), and Europe (EU and EC; DGs, FP6/7) Our mission is to help society by offering scientifically sound advice S. Maerivoet Congestion under Investigation 5

6 The Data Enrichment Group Background for the DEG: TML is often in a B2B context asked to identify future issues and opportunities There is an increased availability of extensive, wide-ranging mobility data, often available in-house or public databases This prompts the need for dedicated data analyses How can your data create an added value? These databases help enlarge the insights in your company s knowledge and operations We propose to cooperate with you, to enhance and enrich the data, resulting in an increased potential This can be directly translated to an added value for your company S. Maerivoet Congestion under Investigation 6

7 Versatile, accurate, correct, and open Almost all our studies can be found online! Newsletter: Our relations: Telematics Cluster/ITS Belgium: member + Board of Directors. Vlaams Instituut voor Mobiliteit (VIM): member + expert in advisory council Active member of the Vlaamse Stichting Verkeerskunde (VSV) The International Association for the History of Transport, Traffic and Mobility: member EC FP7: external experts S. Maerivoet Congestion under Investigation 7

8 TML is regularly featured in the press Het Nieuwsblad, De Morgen, De Standaard, Het Laatste Nieuws, Knack, Jobat, Le Soir, La Meuse, De Lloyd, Verkeersspecialist, Vacature Magazine, Ademloos, Het Belang van Limburg, Mobimix, De Zondagskrant, De Streekkrant, Verkeersnet.nl, S. Maerivoet Congestion under Investigation 8

9 Overview Introducing Transport & Mobility Leuven (TML) S. Maerivoet Congestion under Investigation 9

10 What causes congestion? Too many people want to drive on the same time at the same location Or: The capacity of the road network is limited They cannot process all the traffic This causes jams leading to delays On top of that: Accidents can cause congestion (and vice versa) In cities congestion arises due to intersections and traffic lights S. Maerivoet Congestion under Investigation 10

11 On the origin of congestion: 2 different theories The European (German) school: Ghostjams out of nothing (Treiterer en Myers, 1974) Kerner, Konhäuser en Rehborn (1994) Helbing (1999) The Berkeley school (University of California): All congestions arises due to bottlenecks There is always a geometric explanation for a jam Newell, Daganzo, Bertini, Cassidy, Muñoz, S. Maerivoet Congestion under Investigation 11

12 How large is the congestion problem? During the morning rush hour on Flemish motorways: o There is ~ 139 km congestion (losing ½ hour) o We all loose ~ 21,500 hours together o This costs ~ 300,000 euro to society On the underlying road network: 4x worse! 91% of the congestion is in Flanders Sources: TML (2008, 2011) S. Maerivoet Congestion under Investigation 12

13 Land use & socio-economic behaviour The demand for transport originates out of the wish to participate at spatially separated social/cultural/economic/ activities CBD = central business district I = industriezone L/M/H = lage/midden/hoge inkomensklasse C = commuter zone Trend towards geosimulation S. Maerivoet Congestion under Investigation 13

14 Trip-based transport model Travellers take decisions that lead to a trip-based model trip generation (how many trips?) Aggregation! trip distribution (where are they going?) modal split (by which means of transport?) traffic assignment (which routes do they take?) The four-step model S. Maerivoet Congestion under Investigation 14

15 Trip-based transport model Travellers take decisions that lead to a trip-based model trip generation trip distribution modal split Route choice is governed by Wardrop/Nash criteria: (1952) User equilibrium (W1) traffic assignment System optimum (W2) S. Maerivoet Congestion under Investigation 15

16 Traffic assignment: calculation of equilibria Formalised by Beckmann, McGuire, and Winsten: The BMW-trio Studies in the Economics of Transportation (1956) Calculations: Convex optimisation theory (quadratic programming) Non-linear congestion functions (e.g., BPR functions) Shortest path algorithms Additions: Stochasticity Risk Non-rational, nor all-informed travellers S. Maerivoet Congestion under Investigation 16

17 Dynamic traffic assignment: the holy grail Static: Use a simplification of road capacity Typically calculates a complete morning rush hour in 1x ( all traffic is simultaneously put on the network ) Dynamic: DTA = dynamic traffic assignment Static Dynamic 7:00 10:00 15:00 18:00 Congestion is a dynamic phenomon in time and space Allows to model blocking back of queues Choice of route and departure time Dynamic network loading (DNL): Analytic Based on simulations (convergence via iterative relaxation) S. Maerivoet Congestion under Investigation 17

18 Activity-based transport model Generation of a synthetic population Generation of plans of activities Executing these activities = physical movement of the agents activity pattern multi-agent systems S. Maerivoet Congestion under Investigation 18

19 Macro-/mesoscopic flow models Describe how traffic physically propagates on a road Based on partial differential equations (i.e., conservation laws) High aggregation, low level of detail Macroscopic: fluid-dynamic models that consider traffic as a compressible fluid (Navier-Stokes) Mesoscopic: gas-kinetic models that consider traffic as a many-particles system: derivation of macroscopic equations from microscopic driver behaviour (e.g., speed distributions) S. Maerivoet Congestion under Investigation 19

20 The LWR macroscopic model Lighthill-Whitham-Richards 1 st order model Traffic = non-viscous compressible fluid Based on a scalar conservation law of density (k) and intensity (q): Assumption: intensity is a function of the density! Relation: Fundamental diagram S. Maerivoet Congestion under Investigation 20

21 LWR: solutions and variations Didactically rich: can be solved graphically Also accurate numerical approximations possible Variations: multi-class, rabbits and slugs, S. Maerivoet Congestion under Investigation 21

22 The fundamental diagram Dates back to Greenshields (1935): Not crowded (fast) Crowded (slow) S. Maerivoet Congestion under Investigation 22

23 #vehicles/hour The fundamental diagram Theory versus practice: #vehicles/kilometre S. Maerivoet Congestion under Investigation 23

24 Capacity drop Different theories, S. Maerivoet Congestion under Investigation 24

25 Microscopic flow models Describe explicitly the interactions of vehicles in a traffic stream (seems more realistic) Low aggregation, high level of detail Car-following model Stimulus-response Optimal velocity Psycho-physical distance Cellular automata Queueing theory follower Leader(s) (n) (n 1) space gap Submicroscopic flow models encompass physical characteristics such as engine performance, gear shifting,... and human decisions (non-strategic) S. Maerivoet Congestion under Investigation 25

26 Microscopic flow models Describe explicitly the interactions of vehicles in a traffic stream (seems more realistic) Low aggregation, high level of detail Car-following model Stimulus-response Optimal velocity Psycho-physical distance Cellular automata Queueing theory Lane-choice model Gap-acceptance Mandatory versus discretionary lane change Submicroscopic flow models encompass physical characteristics such as engine performance, gear shifting,... and human decisions (non-strategic) S. Maerivoet Congestion under Investigation 26

27 Eye for detail S. Maerivoet Congestion under Investigation 27

28 Convincing visualisations S. Maerivoet Congestion under Investigation 28

29 Computersimulations of traffic You can say what you want, but there just models of reality Reality is just another model They have their limitations and reproduces what we feed them Be mindful when policy makers interprete your results The available commercial packages are transport planning models, mainly based on the four-step model Scale: local, network, regional, national, transnational S. Maerivoet Congestion under Investigation 29

30 Overview Introducing Transport & Mobility Leuven (TML) S. Maerivoet Congestion under Investigation 30

31 Possibilities for traffic management Travellers change their departure time (depart earlier, arrive later, not making the journey, ) Flexible hours, work at home: if possible, JIT, shifts,... Road user charging (smart mobility, tollcordons, ) Management of parkings, dynamic car sharing such as car pooling and even real-time ride sharing, Peak lanes, public transport uses dedicated lanes (if ample capacity remains for the remaining traffic) Detection of fog, snow, heavy rain, S. Maerivoet Congestion under Investigation 31

32 Incorporating ICT Advanced Systems (ATMS): Intelligent Transportation Systems (ITS) Automatic incident detection Intelligent traffic lights Dynamic route information panels (DRIPs) aka. Variable message signs (VMSs) Variable speed limits (VSLs) Ramp metering Advanced Traffic Information Systems (ATIS): Radio broadcasts, navigation devices, parking information, Travel time predictions Public transport routing S. Maerivoet Congestion under Investigation 32

33 Intelligent traffic lights Goal: tune intersections with each other, keep traffic on main axes flowing, less emissions of pollutants in canyons (e.g., Wetstraat) Leading to high returns: Green waves for throughput on priority routes Indication of remaining red-/green cycle times Now: supercomputer has a city-wide control S. Maerivoet Congestion under Investigation 33

34 Variable speed limits on DRIPs upstream moving shockwave S. Maerivoet Congestion under Investigation 34

35 Variable speed limits on DRIPs In many cases a standard control algorithm is used Dangerous situations at e.g. Craeybeckxtunnel (E19): 3h at night 50 km/h. Idem at Kennedytunnel We can do better! Cf. KUL study: +8 to15% S. Maerivoet Congestion under Investigation 35

36 ATMS/ATIS: ramp metering The flow is drop by drop trickle-controlled DOEL: verkeer in het stabiel regime houden Sources: KUL-ESAT + TU Delft (2008, 2011) S. Maerivoet Congestion under Investigation 36

37 Traffic safety: Heinrich pyramid ( factor 10 ) Aantal ongevallen met lichtgew Aantal ongevallen met zwaargew Aantal ongev met doden 30 d Vlaams Gewest Waals Gewest Brussels Hoofdstedelijk Gewest België H.W. Heinrich (1939), Industrial Accident Prevention S. Maerivoet Congestion under Investigation 37

38 The real work today is for the almost-accidents Underestimated! Already better safety due to: Better vehicle construction (wrinkle zones, energy dissipation, lower impuls, ) Existing ADAS Focus on: Extra supporting measures Traffic education Enforcement Zero-fatality is practically unattainable Good effectiveness by considering the psychology of humans S. Maerivoet Congestion under Investigation 38

39 Encouraging Urban Transport and Innovation at the Local Level Smart traffic management (traffic light control): Via supercomputers (tendered) Agent-based self-learning (research) Adopting open standards: Focus lies on ICT and ITS to: Improve traffic flow Enhance traffic safety Reduce emissions But also spatial planning! Increasing trend towards Mobility-as-a-Service S. Maerivoet Congestion under Investigation 39

40 About data: the requirement to open up EC Directives EC ITS Directive (2010/40/EG) EC PSI Directive (2013/37/EU) [REUSE] EC INSPIRE Directive (2007/E/EC) Mandatory ITS Action Plans for MS (ITS Action Plans for Cities) Uniform implementation via Delegated Acts Emerging Open Data movements and PPPs Especially at the local level of Cities S. Maerivoet Congestion under Investigation 40

41 A city s first step: making data available to the public Example: Brussels Created an Open Data Portal 508 open data sets (incl. metadata!) Search functionality Access through API (JSON) Specific licence governing (re-)use Citation Liability waiver No previous IPR on the data Events Hackathon (best prototype/business model/data use) GirlsCodeEU workshop 17-18/10/2014 S. Maerivoet Congestion under Investigation 41

42 Next step: sharing ideas and solutions Example: Ghent Upgraded portal: Access through API (JSON, XML, CSV, KML, ) Including dynamic information (real-time parking occupancies) Developers: Can share/supply their own app Can propose ideas Note: just making the data available is not enough to incentivise the market S. Maerivoet Congestion under Investigation 42

43 Incentivising developers Example: Antwerp Over 270 datasets Provide better services Stimulate the creative economy Fusing data Simple licencing (cf. Brussels) Incl. catalogue of available/shared apps Organise a yearly challenge: Apps for Antwerp Best developed app Best concept Distinction between amateurs and professionals S. Maerivoet Congestion under Investigation 43

44 Example results of such a challenge Tracking moving signs Quality of living in a neighbourhood The emotional state of a location Let s fix it and Pinitag Signal older, damaged, or annoying locations Citizens can propose to help ACleanCity: centralises info wrt. waste sorting, disposal, and collection Where to put (extra) garbage bins? Locations of (public) toilets for disabled persons Geoplus: easier access to GIS data S. Maerivoet Congestion under Investigation 44

45 Going even further: ACPaaS ACPaaS = Antwerp City Platform as a Service The goal is to re-use components and prevent from creating or buying them over and over again Develop the platform in cooperation with startups Reaching out via Meetups Stimulate co-creation and innovation Separate apps (changing) front-ends from their back-ends Back-ends move into (stable) engines Accessible through APIs Everyone can participate in building a digital city! S. Maerivoet Congestion under Investigation 45

46 Open Data in cities is a global phenomenon Various emerging Open Data movements and PPPs Flanders Open Data Day (incl. funded innovation projects) Triangle Open Data Day Open Data Education Hacking for civic good International Open Data Hackathon Write applications Liberate data Create visualisations Publish analyses Open Data Meetups: Education Sharing ideas Incentivise politicians S. Maerivoet Congestion under Investigation 46

47 Overview Introducing Transport & Mobility Leuven (TML) S. Maerivoet Congestion under Investigation 47

48 Macroscopic effects by means of OmniTRANS Relocation ISVAG waste furnace Closure of the R1 ring around Antwerp (Meccano BAM trajectories) S. Maerivoet Congestion under Investigation 48

49 Microscopic effects by means of Paramics Accessibility Carrefour Korbeek-Lo + Herstal Tunnel under the Waterloolaan in Brussels Masterplan Antwerp (Moerelei) S. Maerivoet Congestion under Investigation 49

50 Visualising measurements: counts at intersections Inleiding Modellen van verkeersstromen Verzamelen van verkeersgegevens Regelen van verkeersstromen Modellen en studies (turn-fractions) (traffic signal plans) D Q E S. Maerivoet Congestion under Investigation 50

51 SUSTAPARK: sustainable parking with a TCA model Goal: simulate the effects of changes in the parking situation and parking policy of a city (Change the number of parking spaces, the price and duration of public parking, on-street versus below ground parking, ) Main components: Modelling the parking demand Modelling the search behaviour Modelling the economic equilibrium S. Maerivoet Congestion under Investigation 51

52 Studies based on traffic measurements Traffic indices on Belgian motorways Analysis of the congestion in Belgium Impact of reduced maximumspeeds on motorways How much time is spent in congestion during a career? S. Maerivoet Congestion under Investigation 52

53 Fixed datasources Single/double inductive loop detectors in the pavement, radars, cameras, pneumatic tubes,... S. Maerivoet Congestion under Investigation 53

54 Visualising measurements: patterns Working days Weekend S. Maerivoet Congestion under Investigation 54

55 Clustering measurements: patterns S. Maerivoet Congestion under Investigation 55

56 Historical patterns are essential to understand morning rush hour evening rush hour Morning: Heavy jams at Vilvoorde and Strombeek-Bever Slower traffic at the junction with the E19 and E40 Evening: Heavy jams at the Vierarmenkruispunt, Tervuren and Wezembeek-Oppem S. Maerivoet Congestion under Investigation 56

57 The impact of an average speed control Light offenders Heavy offenders % less accidents 23% less wounded 43% less car-wounded feb mrt apr mei jun jul aug sep okt nov dec jan apr mei jun jul aug sep okt nov dec jan feb mrt Before implementation After implementation S. Maerivoet Congestion under Investigation 57

58 Insight into quality of measurements S. Maerivoet Congestion under Investigation 58

59 A new trend is being set S. Maerivoet Congestion under Investigation 59

60 Want some data!? Yes, big and open please! S. Maerivoet Congestion under Investigation 60

61 Mobility just seems Large, but is becoming Big: patterns S. Maerivoet Congestion under Investigation 61

62 Technology: validation of CFVD (GSM) Tracking of hand-overs at cell boundaries during conversations BTS BTS BTS BTS BTS destination origin travel time BTS segments BTS BTS hand-over GSM probe road S. Maerivoet Congestion under Investigation 62

63 Higher accuracy: GPS GPS-probe vehicles (e.g. trucks, lease cars, taxis, ) NASA GPS (24 satellites) / Europe (ESA) Galileo S. Maerivoet Congestion under Investigation 63

64 From large data to really big data The new generation of mobile data GSM is kinda out, GPS is more than in! Less and less problems with estimation of traffic volumes. Fusion with available measurements (via traffic centres) Going beyond (X-)FCD: E.g., mobility patterns from Bluetooth-scanners, Twitter feeds, Android locations, S. Maerivoet Congestion under Investigation 64

65 Micro level: speeding infractions Detailed speed measurements can be correlated with reigning traffic speed limits at specific locations Right-of-way, following distances, interactions, S. Maerivoet Congestion under Investigation 65

66 The first signs of autonomy Radio controlled in 1926! : Strongly dependent on infrastructure From 2004 on: DARPA Grand Challenge 240 km in the Mojave desert Heavily equipped vehicles (The Milwaukee Sentinel, 1926) S. Maerivoet Congestion under Investigation 66

67 The long run to autonomous vehicles Advanced Driver Assistance Systems (ADAS) 1. Safe speeds and following distances 2. Lane guiding 3. Detection of obstacles and collision avoidance 4. Satefy of intersections and complex situations Tools! Autonomous driving (A Quick Scan of Quantified Effects of ADAS ADASE II Extension, 2004) S. Maerivoet Congestion under Investigation 67

68 Evolution in legislation Locomotives Red Flag Acts (1865) Max 3 km/h in cities Required: driver + stoker + flag Background: protection of rail and horse carriage industries Vienna Convention wrt. road traffic (1968) S. Maerivoet Congestion under Investigation 68

69 Different levels of autonomy Level 0 (no autonomy): Driver always takes actions Level 1 (driver task support): Steering or deceleration/acceleration are controlled Level 2 (partial autonomy): Steering and deceleration/acceleration are controlled Level 3 (conditional autonomy): Surroundings + intervention of the driver is still required Level 4 (high autonomy): Partial control (even if driver does not interfere) Level 5 (full autonomy): Full control of all driving tasks National Highway Transportation Safety Agency (NHTSA, 2013) Society of Automotive Engineers (SAE, 2013) (Fleet renewal every 16 year) (SAE, 2013) S. Maerivoet Congestion under Investigation 69!!!

70 Autonomy with integration of V2V and V2I (V2X) VANETs (mesh grids) + cooperative driving Communication with (intelligent) intersections Note: Google Car contained a very detailed map S. Maerivoet Congestion under Investigation 70

71 EMDAS (collective of Flemish companies/research institutes) Goal: develop a longitud. self-driving bus (v < 30 km/h) TML develops an objective framework for the assessment of safety impacts: Compare accident risk with and without AVs Change in accident risk ifo. the level of autonomy Understand the interactions between driver and vehicle Insight into unsafe behaviour that follows from this Need on a dedicated analysis of accidents S. Maerivoet Congestion under Investigation 71

72 Analyse common accident schemadata (CADAS / GIDAS) Derive accident typologies Validate with existing research (BIVV, VSV, IMOB, ) We get equipped vehicles Simulation test Provide feedback (sensor specs.) S. Maerivoet Congestion under Investigation 72

73 Creating PreScan experiments S. Maerivoet Congestion under Investigation 73

74 Beyond longitudinal control: safety at intersections S. Maerivoet Congestion under Investigation 74

75 Integration with public transport: co-modality 200, , , , , ,000 80,000 60,000 40,000 20, Working days Saturdays Sundays S. Maerivoet Congestion under Investigation 75

76 Discovering travel patterns beyond classic OVG S. Maerivoet Congestion under Investigation 76

77 A behavioural experiment with road user charging S. Maerivoet Congestion under Investigation 77

78 Peak shifting due to road user charging People drove less during the morning rush hour, more during the day and later during the evening rush hour 11.00% 10.00% 9.00% 8.00% 7.00% 6.00% 5.00% 4.00% 3.00% 2.00% 1.00% 0.00% 00u 01u 02u 03u 04u 05u 06u 07u 08u 09u 10u 11u 12u 13u 14u 15u 16u 17u 18u 19u 20u 21u 22u 23u Fase 1 (nulmeting) Fase 2 (wedstrijd) S. Maerivoet Congestion under Investigation 78

79 Parking management in cities S. Maerivoet Congestion under Investigation 79

80 Macro level: EV potential Which trips can be substituted by an electric vehicle? Based on vehicle usage patterns Trip distance (~ range) vs. inter-trip time (~ recharging) Different perspectives: Market potential Car ownership S. Maerivoet Congestion under Investigation 80

81 An new generation of urban route planning and parking Next-generation route planners: Incl. green and predictive routing Incl. tourist planning Culminating in: Incl. Integrated Fare Management (IFM) Smart parking apps: S. Maerivoet Congestion under Investigation 81

82 Sharing Knowledge and Best Practices Exchanging via EU programmes and platforms S. Maerivoet Congestion under Investigation 82

83 Protecting identities S. Maerivoet Congestion under Investigation 83

84 Overview Introducing Transport & Mobility Leuven (TML) S. Maerivoet Congestion under Investigation 84

85 Theory of cellular automata Cells in a discrete space (e.g., 2D plane): The neighbours of cells (von Neumann vs. Moore): S. Maerivoet Congestion under Investigation 85

86 Theory of cellular automata The local transition rule: S. Maerivoet Congestion under Investigation 86

87 The history of cellular automata 1948: von Neumann on self-reproduction Ulam introduces cellular spaces 1952: Turing talks about morphogenesis 1970: John Conway s Game of Life S. Maerivoet Congestion under Investigation 87

88 The history of cellular automata 1983: Wolfram s A New Kind of Science : Zuse and Fredkin state that the universe is a CA S. Maerivoet Congestion under Investigation 88

89 Modelling traffic cellular automata (TCA) Conversion of continuous to discrete time/space: S. Maerivoet Congestion under Investigation 89

90 Modelling traffic cellular automata (TCA) Faster (but coarser) version of microscopic models space (e.g., grid of cells) t upstream downstream Δt v = 3 v = 1 t + 1s x(t + 1) = x(t) + v(t + 1) time Car-following submodel = set of local transition rules S. Maerivoet Congestion under Investigation 90

91 Modelling traffic cellular automata (TCA) Faster (but coarser) version of microscopic models space (e.g., grid of cells) t upstream downstream Δt v = 4 v = 1 t + 1s x(t + 1) = x(t) + v(t + 1) time v = 2 Car-following submodel = set of local transition rules S. Maerivoet Congestion under Investigation 91

92 Modelling traffic cellular automata (TCA) Faster (but coarser) version of microscopic models space (e.g., grid of cells) t upstream downstream Δt v = 4 v = 1 t + 1s x(t + 1) = x(t) + v(t + 1) time v = 3 Car-following submodel = set of local transition rules v = 2 S. Maerivoet Congestion under Investigation 92

93 Visualising congestion in a cellular automaton E.g., vehicles are driving round a circle: time-space diagram! trajectory S. Maerivoet Congestion under Investigation 93

94 Wolfram s regel CA-184 Simple, 8 possible transitions: Time-space diagrams: not crowded Getting crowded very crowded congestion wave S. Maerivoet Congestion under Investigation 94

95 Variations of cellular automata Velocity-dependent randomisation model Nagel-Schreckenberg model Knospe s model with brake lights spontaneous jams! S. Maerivoet Congestion under Investigation 95

96 Complexity of the rules: a TCA with brake lights Random slowing down: randomisation Slow-to-start behaviour: capacity drop Anticipation effects: stabilisation of free-flowing traffic human behaviour S. Maerivoet Congestion under Investigation 96

97 A look on the fundamental diagram in a TCA Helbing-Schreckenberg Kerner-Klenov-Wolf Knospe et al. ( brake-lights ) S. Maerivoet Congestion under Investigation 97

98 More lanes and city traffic Multi-lane traffic : Mandatory versus discretionary lane change Culture: keep-your-lane vs. mandatory driving on the right Phenomena: speed differences, density inversions, Same driving directions versus bidirectional traffic Watch out for ping-pong traffic! City traffic: Classic Biham, Middleton, and Levine (BML) model Explicit intersections, roundabouts, S. Maerivoet Congestion under Investigation 98

99 Transportplanning with TCA models TRANSIMS: TRansportation ANalysis SIMulation System Activity- and agent-based MATSim: Multi-Agent Traffic Simulation Activity- and agent-based Even faster: queueing models Finite buffers! S. Maerivoet Congestion under Investigation 99

100 More information? Modelling Traffic on Motorways (16) Transport & Mobility Leuven: Data Enrichment Group: S. Maerivoet Congestion under Investigation 100